When traditional nozzles are used for workpiece finishing

the jet leaves the nozzle

concentrating its energy at the central position

and the one-time processing area is small. As a result

although the overall surface roughness of the workpiece after finishing is effectively reduced

it tends to cause local deformation of the workpiece and the problem of poor uniformity. In order to further improve the surface quality and uniformity of thin-walled and complex workpieces processed by abrasive jet finishing

and to impro

ve the efficiency of the finishing process. By changing the shape of the nozzle outlet

and then change the structure of the jet

so that the jet energy distribution is more uniform

to improve the effect and feasibility of abrasive jet finishing. Fluent software was used to analyse the jet structure of the nozzle

abrasive trajectory

erosion and shear effect

to explore the advantages of the in-line nozzle finishing

to verify the finishing effect of the in-line nozzle through the test

and to analyse the influence of the various influencing factors on the effect of the finishing

and finally to establish the BP neural network prediction model and particle swarm parameter optimisation

to find the optimal parameters of the finishing. The in-line nozzle can effectively improve the surface quality and uniformity of the abrasive jet finishing process

improve the efficiency of the abrasive jet finishing process

and in the case of the same mass flow rate

the in-line nozzle has less influence on the deformation of the workpiece. Finally

simulation analysis and experiments show that the slotted nozzle can effectively improve the surface quality and reduce the surface roughness of abrasive jet finishing

improve the efficiency of abrasive jet finishing

and reduce the influence of jet processing on the workpiece. Through the prediction model constructed by BP neural network and the optimization of particle swarm parameters

when the processing time is 15 min

the abrasive particle size is 20 μm

the target distance is 12 mm

and the pressure is 0.08 MPa

the surface roughness of the aluminum alloy after finishing is reduced from R

a

0.513 μm to R

a

0.219 μm

and the surface roughness is basically the same as that measured in the vertical direction. Experiments verify that the BP neural network prediction model has high accuracy.